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Role of Stochastic Forcing inENSO variability in a coupled GCM
Atul KapurChidong Zhang
Javier Zavala-Garay
Acknowledgements: Ben Kirtman, Amy Clement
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• Stochastic Forcing (SF)– Atmospheric variability uncoupled to the ocean
Annual Cycle
• Extent to which the ENSO in CGCMs is driven by SF• Contributions of Madden Julian Oscillation (MJO) and non-MJO• Dynamical regime of underlying coupled system – Stable or
Unstable
Introduction
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ProcedureENSO
SFSF SFSF
ENSO
CZZ model
ENSO ENSO
Extract Extract
CompareCompare CompareCompareRole of SF
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Model and Data
Variant of Zebiak and Cane (1987) model• Chaos switched off (Mantua and Battisti 1995)
• Admits daily SF: Decorrelation time of tropical weather ~ 3-8 days
Bureau of Meteorology Research Center (BMRC) CGCM (Zhong et al. 2004)
• A 163-year run• Realistic ENSO (Wu et al. 2002) and intraseasonal
variability (Zhang et al. 2006)
NCEP-2 Reanalysis (1979-2007)
CZZ model
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ProcedureENSO
SFSF SFSF
ENSO
CZZ model
ENSO ENSO
Extract Extract
CompareCompare CompareCompareRole of SF
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Stochastic Forcing• Statistical model of u10 anomalies predicted by SST anomalies
u10 = A sst + uResidual
• Wavenumber frequency spectra:
Zonal wavenumber
Period
Inter-annual
Intra-seasonal
Coupled Residual MJO
Caveats: Linear, Contemporaneous, Additive(CGCM)
(Hilbert EOF)
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ProcedureENSO
SFSF SFSF
ENSO
CZZ model
ENSO ENSO
Extract Extract
CompareCompare CompareCompareRole of SF
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Simulations using NCEP-2 SFPower Spectra
• CZZ model able to reproduce spectrum• ENSO statistics better for MJO than non-MJO forcing• CZZ model performs best in weakly stable regime
CZZ95 % confid
NCEP-2
0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0
Freq (cycles/yr)
Pow
er *
freq
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Simulations using NCEP-2 SFSeasonal Variance
CZZ warmNCEP-2 warm
CZZ coldNCEP-2 cold
• Warm phase better simulated than cold phase in terms of seasonal variance
3
2
1
0
-1
-2J F M A M J J A S O N D
Normalizedvariance
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Simulations using NCEP-2 SFSeasonal Autocorrelation
Total MJO Non-MJO
Lag (month)
D
O
A
J
A
F
Startingmonth
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ProcedureENSO
SFSF SFSF
ENSO
CZZ model
ENSO ENSO
Extract Extract
CompareCompare CompareCompareRole of SF
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Simulations using CGCM SFPower Spectrum
• SF is able to reproduce even local peaks in power spectrum• Results using MJO compare better to “truth” than non-MJO
CZZ95 % confid
CGCM
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Simulations using CGCM SFSeasonal Variance
Total SF MJO Non-MJO CZZ warmCGCM warm
CZZ coldCGCM cold
• SF unable to reproduce the seasonal variance of ENSO exhibited by the BMRC CGCM
• Contribution of non-MJO appears to be higher than MJO
Norm.variance
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Simulations using CGCM SFSeasonal Autocorrelation
Total MJO Non-MJO
D
O
A
J
A
F
Startingmonth
Lag (month)
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ProcedureENSO
SFSF SFSF
ENSO
CZZ model
ENSO ENSO
Extract Extract
CompareCompare CompareCompareRole of SF
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Conclusions• Role of SF in BMRC CGCM ENSO– At least the warm phase can be reasonably
simulated using SF– MJO contribution is higher than non-MJO– Underlying dynamical state of coupled system
appears to be weakly stable– Seasonality of ENSO cannot be reproduced by SF
• Procedure can be implemented on any CGCM– Even on runs with long temporal span